Insurance underwriters have the ongoing problem of not being able to accurately predict or assess future underwriting risks in a given book of insurance policies, and in particular, are not able to do so in a way that further eliminates or reduces undesirable risk policies before they actually become a risk problem. Although risk scoring assists with this prediction, it is not always sufficient to answer the profitability problem and also introduces additional challenges to consistently answering the same. There are many reasons for this. First, typical scoring processes, which presumably address such indicia such as accident history, credit score, insurable asset type, locations, etc. can vary extensively, and even randomly, over time. This is problematic, because if indicia are to be used as risk assessments in the underwriting process, the inherent variability that may occur over time must be considered in the score to produce an accurate risk level assessment (whether positive or negative) for a given policy.
To compound this problem, these imperfect, (essentially) static scores are not perfectly analogous across macro- and micro-geographical conditions. Under these conditions, what may typically be considered an acceptable risk in, for example, the state of Massachusetts is not the same thing as an “acceptable” risk in Barnstable County, Mass., where the types and frequency of claims (e.g., in Barnstable County there may be a particularly high number of fraudulent auto accidents), when taken as a whole, are much more severe, and much greater in magnitude than elsewhere in Massachusetts. When taken on a macro level, categories of risk across the country (e.g., between one state to another state) are diluted by the inherent variability of risks particular to the many regions and sub-regions and the relative differences in scores. Even if one were to attempt to add surcharges or other static adjustments to policies on a countrywide or statewide level, this would not accurately predict the likelihood of policies becoming an unacceptable risk. Furthermore, downstream projected assessments of a given policy such as projected profitability, or “projected loss ratios” are distorted by these variations if not properly reflected in the score calibration.